Cardiac Arrhythmias Identification by Parallel CNNs and ECG Time-Frequency Representation
Por:
Torres, Jonathan R., De los Rios, K., Padilla, Miguel A.
Publicada:
1 ene 2020
Resumen:
Heart abnormalities cause about 26 % of the deaths of illnesses in the
world. Developing computational tools for ECG interpretation plays a
critical role in the clinical diagnosis of Cardiac arrhythmias (CAs).
Aims: This study aimed to develop an automated abnormal pattern
recognition method for clinical decision support capable of detecting
between 27 possible CAs. Proposal: An improved deep learning (DL) model
was employed using raw-data and time-frequency representation (TFR)
images. Methods: A vast set of ECG records were filtered and normalized.
They were segmented and transformed into two sets of 2-D images. TFR
images were obtained through Wavelet Synchrosqueezing (WS). The VGG-16
network was chosen, modifying the weights of the inner layers to adapt
the model to the CAs detection task. A 10-fold cross-validation method
was executed. Different training hyperparameters were tested to find the
best model. Results: With the cross-validation on the training data, the
model developed by our team UIDT-UNAM performed identifying CAs, with an
overall unofficial S-score of 0.766. This model had a high performance
in detecting healthy subjects with an F1 score of 0.83. We obtained
these results using only the public training dataset. We plan to test
these optimistic results with Physionet private dataset very soon.
Filiaciones:
Torres, Jonathan R.:
Institute of Applied Sciences and Technology, Universidad Nacional Autónoma de México, Cto. Exterior, Cd. Universitaria, Mexico City, 04510, Mexico
De los Rios, K.:
Institute of Physics, Universidad Nacional Autónoma de México, Mexico
Padilla, Miguel A.:
Institute of Applied Sciences and Technology, Universidad Nacional Autónoma de México, Cto. Exterior, Cd. Universitaria, Mexico City, 04510, Mexico
Bronze, All Open Access; Bronze
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